System and method for machine-learning input-based data autogeneration

ABSTRACT

Systems, methods, and devices for automated provisioning are disclosed herein. The system can include a memory including a user profile database having n-dimension attributes of a user. The system can include a user device and a source device. The system can include a server that can: generate and store a user profile in the user profile database and generate and store a characterization vector from the user profile. The server can identify a service for provisioning, receive updates to at least some of the attributes of the first user, and trigger regeneration of the characterization vector from the received inputs. The server can: regenerate the characterization vector, determine an efficacy of the provisioned services, and automatically identify a second service for provisioning for a second user based on the efficacy of the provisioned services to the first user.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/641,974, filed on Mar. 12, 2018, and entitled “STATE-BASED CAREPROVISION LEARNING SYSTEM”, and U.S. Provisional Application No.62/486,358, filed on Apr. 17, 2017, and entitled, “STATE-BASED CAREPROVISION LEARNING SYSTEM”, the entirety of each of which is herebyincorporated by reference herein.

BACKGROUND

A computer network, or data network, is a digital telecommunicationsnetwork which allows nodes to share resources. In computer networks,computing devices exchange data with each other using connectionsbetween nodes (data links.) These data links are established over cablemedia such as wires or optic cables, or wireless media such as WiFi.

Network computer devices that originate, route and terminate the dataare called network nodes. Nodes can include hosts such as personalcomputers, phones, servers as well as networking hardware. Two suchdevices can be said to be networked together when one device is able toexchange information with the other device, whether or not they have adirect connection to each other. In most cases, application-specificcommunications protocols are layered (i.e. carried as payload) overother more general communications protocols. This formidable collectionof information technology requires skilled network management to keep itall running reliably.

Computer networks are increasingly used in diverse applications.However, many current networks do not adequately address issues arisingin an increasingly digital world. Accordingly, new and improved systems,methods, and devices are desired.

BRIEF SUMMARY

One aspect of the present disclosure relates to an automatedvector-characterization system. The system includes a memory including auser profile database containing n-dimension attributes of a user. Thesystem includes a user device that can receive input from a first userand transmit the received input, and a source device that can receivesource inputs and transmit the received source inputs. The system caninclude a server. The server can: receive the user input and the sourceinputs; generate and store a user profile in the user profile database,which user profile identifies n-dimension attributes of the first user;generate and store a characterization vector from the n-dimensionattributes of the first user, which characterization vector can includefour dimensions, each of which four dimensions can include a gradatedclassification indicator; identify a service for provisioning accordingto the characterization vector, the service including a digitalcomponent and a non-digital component; receive inputs indicating updatesto at least some of the n-dimension attributes of the first user, whichupdates are received at least from the source device; triggerregeneration of the characterization vector from the received inputs;regenerate the characterization vector from the n-dimension attributesof the first user, which n-dimension attributes of the first userinclude the updates to at least some of the n-dimension attributes;generate a discrepancy vector characterizing a difference between theregenerated characterization vector and the characterization vector;determine an efficacy of the provisioned services based on thediscrepancy vector; and automatically identify a second service forprovisioning for a second user based on the efficacy of the provisionedservices to the first user.

In some embodiments, the server can automatically deliver the digitalcomponent of the service for provisioning. In some embodiments, thedigital component of the service for provisioning is automaticallydelivered subsequent to automated determination of fulfillment of atleast one delivery criteria. In some embodiments, the determination offulfillment of the at least one delivery criteria is made based on datacontained in a data stream received from a messaging bus. In someembodiments, aspects of the characterization vector are linked to aplurality of services via a plurality of conditional probabilities. Insome embodiments, the aspects of the characterization vector linked tothe plurality of services via the plurality of conditional probabilitiesinclude at least some of the n-dimension attributes of the first user.

In some embodiments, the service for provisioning is identifiedaccording to an AI machine-learning model. In some embodiments, the AImachine-learning model is trained to output a service for provisioningbased on the characterization vector. In some embodiments, the servercan ingest at least a portion of the characterization vector into the AImachine-learning model. In some embodiments, the AI machine-learningmodel is trained to output a service intensity based on thecharacterization vector. In some embodiments, the four dimensionsinclude: a physical dimension; an emotional dimension; an interactiondimension; and a vulnerability dimension.

One aspect of the present disclosure relates to a method for automatedcharacterization-vector based prediction. The method includes: receivinga user input from a user device and a source input from a source device;generating and storing a user profile in a user profile database, whichuser profile identifies n-dimension attributes of a first user;generating and storing a characterization vector from the n-dimensionattributes of the first user, which characterization vector can havefour dimensions, each of which four dimensions can include a gradatedclassification indicator; identifying a service for provisioning to thefirst user according to the characterization vector, the serviceincluding a digital component and a non-digital component; receivinginputs indicating updates to at least some of the n-dimension attributesof the first user, which updates are received at least from the sourcedevice; triggering regeneration of the characterization vector from thereceived inputs; regenerating the characterization vector from then-dimension attributes of the first user, which n-dimension attributesof the first user include the updates to at least some of then-dimension attributes; generating a discrepancy vector characterizing adifference between the regenerated characterization vector and thecharacterization vector; determining an efficacy of the provisionedservices based on the discrepancy vector; and automatically identifyinga second service for provisioning for a second user based on theefficacy of the provisioned services to the first user.

In some embodiments, the method includes automatically delivering thedigital component of the service for provisioning. In some embodiments,the digital component of the service for provisioning is automaticallydelivered subsequent to automated determination of fulfillment of atleast one delivery criteria. In some embodiments, the determination offulfillment of the at least one delivery criteria is made based on datacontained in a data stream received from a messaging bus. In someembodiments, aspects of the characterization vector are linked to aplurality of services via a plurality of conditional probabilities. Insome embodiments, the aspects of the characterization vector linked tothe plurality of services via the plurality of conditional probabilitiesinclude at least some of the n-dimension attributes of the first user.

In some embodiments, the service for provisioning is identifiedaccording to an AI machine-learning model. In some embodiments, the AImachine-learning model is trained to output a service for provisioningbased on the characterization vector. In some embodiments, the methodincludes ingesting at least a portion of the characterization vectorinto the AI machine-learning model. In some embodiments, the AImachine-learning model is trained to output a service intensity based onthe characterization vector. In some embodiments, the four dimensionsinclude: a physical dimension; an emotional dimension; an interactiondimension; and a vulnerability dimension.

One aspect of the present disclosure relates to an automatedmulti-dimensional network management system. The system includes: amemory including: a user profile database having n-dimension attributesof a user; and a multi-dimensional network having a plurality of nodeslinked by a plurality of edges. The system can include a user devicethat can receive input from a first user and transmit the receivedinput, and a source device that can receive source inputs and transmitthe received source inputs. The system can include a server. The servercan: receive the user input and the source inputs; generate and store auser profile in the user profile database, which user profile identifiesn-dimension attributes of the first user; determine a current state ofthe first user based on the user profile; generate a risk profileaccording to the current state of the first user, which risk profileidentifies a likelihood of an adverse outcome within a time frame;identify a remediation via an AI machine-learning model to mitigate thelikelihood of the adverse outcome, which remediation is identified basedon the user profile and the current state of the first user; identify adata insufficiency, which data insufficiency prevents at least one of:complete determination of the current state of the first user; completegeneration of the risk profile; or complete identification of theremediation; identify a service for provisioning to the first user,which service is identified to remedy the data insufficiency, theservice including a digital component and a non-digital component;provisioning the identified service and resolving the datainsufficiency; automatically determining resolution of the datainsufficiency; upon determining resolution of the data insufficiency,completing the at least one of: determination of the current state ofthe first user; generation of the risk profile; or identification of theremediation; and provide a remediation to the first user.

In some embodiments, the server can automatically deliver the digitalcomponent of the service for provisioning. In some embodiments, thedigital component of the service for provisioning is automaticallydelivered subsequent to automated determination of fulfillment of atleast one delivery criteria. In some embodiments, the server canautomatically schedule provisioning of the service. In some embodiments,scheduling the provisioning of the service includes: retrieving userlocation information from the user device; determining a classificationof the service for provisioning; identifying potential service locationsbased on a combination of location of the potential service locations,user location information, and service location attributes.

In some embodiments, the user location information retrieved from theuser device can include current location information and locationhistory information. In some embodiments, the location historyinformation identifies historic trends in user location. In someembodiments, the service location attributes identify service typesprovided at the service location. In some embodiments, the server can:receive inputs indicating updates to at least some of the n-dimensionattributes of the first user, which updates are received at least fromthe source device; trigger updating of the user profile based on thereceived inputs; and automatically identify a second service forprovisioning to the first user. In some embodiments, the second serviceis identified based on the updated user profile. In some embodiments,the second service can include a plurality of timed and automaticallytriggered reminders directing the first user to complete an action.

One aspect of the present disclosure relates to a method ofmulti-dimensional network management. The method includes: receiving auser input from a first user device and source inputs from at least onesource device; generating and storing a user profile in the user profiledatabase, which user profile identifies n-dimension attributes of thefirst user; determining a current state of the first user based on theuser profile; generating a risk profile according to the current stateof the first user, which risk profile identifies a likelihood of anadverse outcome within a time frame; identifying a remediation via an AImachine-learning model to mitigate the likelihood of the adverseoutcome, which remediation is identified based on the user profile andthe current state of the first user; identifying a data insufficiency,which data insufficiency prevents at least one of: completedetermination of the current state of the first user; completegeneration of the risk profile; or complete identification of theremediation; identifying a service for provisioning to the first user,which service is identified to remedy the data insufficiency, theservice including a digital component and a non-digital component;provisioning the identified service and resolving the datainsufficiency; automatically determining resolution of the datainsufficiency; upon determining resolution of the data insufficiency,completing the at least one of: determination of the current state ofthe first user; generation of the risk profile; or identification of theremediation; and providing a remediation to the first user via the firstuser device.

In some embodiments, the method includes automatically delivering thedigital component of the service for provisioning. In some embodiments,the digital component of the service for provisioning is automaticallydelivered subsequent to automated determination of fulfillment of atleast one delivery criteria. In some embodiments, the method includesautomatically scheduling provisioning of the service. In someembodiments, scheduling the provisioning of the service includes:retrieving user location information from the user device; determining aclassification of the service for provisioning; identifying potentialservice locations based on a combination of location of the potentialservice locations, user location information, and service locationattributes.

In some embodiments, the user location information retrieved from theuser device includes current location information and location historyinformation. In some embodiments, the location history informationidentifies historic trends in user location. In some embodiments, theservice location attributes identify service types provided at theservice location.

In some embodiments, the method includes: receiving inputs indicatingupdates to at least some of the n-dimension attributes of the firstuser, which updates are received at least from the source device;triggering updating of the user profile based on the received inputs;and automatically identifying a second service for provisioning to thefirst user, which second service is identified based on the updated userprofile. In some embodiments, the second service includes a plurality oftimed and automatically triggered reminders directing the first user tocomplete an action.

One aspect of the present disclosure relates to an automatedmulti-dimensional network management system. The system includes amemory including: a user profile database identifying attributes of aplurality of users, and a model database including a plurality ofmachine-learning models trained to generate text in response to receivedinputs characterizing attributes of a subject-user and at least onerecipient-user. In some embodiments, the plurality of users include aplurality of classes of users. In some embodiments, the plurality ofclasses of users includes at least one class of subject-users and atleast two classes of recipient-users. The system can include a sourcedevice that can receive source inputs and transmit the received sourceinputs, which source inputs relate to an attribute of the subject-user.The system can include a recipient device that can receive recipientinputs and transmit the received recipient inputs, which recipientinputs identify an attribute of the recipient-user. The system caninclude a server. The server can: receive a plurality of inputsidentifying attributes of the subject-user; receive at least one inputidentifying an attribute of the recipient-user; select amachine-learning model based on the at least one attribute of therecipient-user; ingest received inputs into the machine-learning model;receive text output from the machine-learning model, which text outputis customized to the recipient-user; and automatically generate anddeliver a message to the recipient-user device comprising at leastportions of the text output.

In some embodiments, the server can to receive a report request for therecipient-user device. In some embodiments, the report request includesdata identifying the attribute of the recipient-user. In someembodiments, at least some of the plurality of inputs identifyingattributes of the subject-user are received from the source device. Insome embodiments, the system includes an electrical analysis machine. Insome embodiments, the electrical analysis automatically generates dataindicative of an attribute of the subject-user from an interaction withthe subject-user. In some embodiments, the electrical analysis machineautomatically provides the data indicative of the attribute of thesubject-user to the server.

In some embodiments, the server can receive feedback data indicative ofa responsiveness of the delivered message to the report request. In someembodiments, the server can receive a second report request from asecond recipient-user. In some embodiments, the second report requestincludes at least one input identifying an attribute of the secondrecipient-user. In some embodiments, the attribute of the secondrecipient-user is different than the attribute of the recipient-user. Insome embodiments, the server can: select a second machine-learning modelbased on the attribute of the second recipient-user; and automaticallygenerate and deliver a second message to the second recipient-userincluding an output of the second machine-learning model. In someembodiments, the server can: ingest the plurality of inputs identifyingattributes of the subject-user into the second machine-learning model;and receive text output from the second machine-learning model, whereinthe text output is customized to the second recipient-user.

One aspect of the present disclosure relates to a method ofmachine-learning input-based data autogeneration. The method includes:receiving at a server a plurality of inputs from a source deviceidentifying attributes of a subject-user; receiving at the server atleast one input from a recipient-user device identifying at least oneattribute of a recipient-user; selecting a machine-learning model from amodel database based on the at least one attribute of therecipient-user, the model database including a plurality ofmachine-learning models trained to generate text in response to receivedinputs characterizing attributes of a subject-user and at least onerecipient-user; ingesting received inputs into the machine-learningmodel; receiving text output from the machine-learning model, which textoutput is customized to the recipient-user; and automatically generatingand delivering a message to the recipient-user device comprising atleast portions of the text output.

In some embodiments, the method includes receiving a report request forthe recipient-user device, which report request includes dataidentifying the attribute of the recipient-user. In some embodiments, atleast some of the plurality of inputs identifying attributes of thesubject-user are received from the source device. In some embodiments,the method includes receiving data identifying of an attribute of thesubject-user at the server from an electrical analysis machine. In someembodiments, the electrical analysis machine automatically generatesdata from an interaction with the subject-user. In some embodiments, theelectrical analysis machine automatically provides the data identifyingthe attribute of the subject-user to the server.

In some embodiments, the method includes receiving feedback dataindicative of a responsiveness of the delivered message to the reportrequest. In some embodiments, the method includes receiving a secondreport request from a second recipient-user. In some embodiments, thesecond report request includes at least one input identifying anattribute of the second recipient-user. In some embodiments, theattribute of the second recipient-user is different than the attributeof the recipient-user.

In some embodiments, the method includes: selecting a secondmachine-learning model based on the attribute of the secondrecipient-user; and automatically generating and delivering a secondmessage to the second recipient-user including an output of the secondmachine-learning model. In some embodiments, the method includes:ingesting the plurality of inputs identifying attributes of thesubject-user into the second machine-learning model; and receiving textoutput from the second machine-learning model. In some embodiments, thetext output is customized to the second recipient-user.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples, whileindicating various embodiments, are intended for purposes ofillustration only and are not intended to necessarily limit the scope ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating various components of anautomated provisioning network.

FIG. 2 is a schematic illustration of one embodiment of a distributedcomputing environment.

FIG. 3 is a schematic illustration of one embodiment of a server of theautomated provisioning network.

FIG. 4 is a schematic illustration of one embodiment of a computersystem.

FIG. 5 is an illustration of one embodiment of a multimodal provisioningsystem.

FIG. 6 is an illustration of an embodiment of a multimodal provisioningengine of the multimodal provisioning system.

FIG. 7 is an illustration of one embodiment of a user management system.

FIG. 8 is a schematic illustration of one embodiment of a servicelocation model.

FIG. 9 is an illustration of one embodiment of a plurality of serviceprovisioning lines.

FIG. 10 is a schematic illustration of an embodiment of a servicelocation.

FIG. 11 is an illustration of one embodiment of an outcome assessmentinterface.

FIG. 12 is an illustration of one embodiment of a summary vector.

FIG. 13 is an illustration of one embodiment of an emotional versusphysical matrix.

FIG. 14 is a flowchart illustrating one embodiment of a process forautomated characterization-vector based prediction.

FIG. 15 is a flowchart illustrating one embodiment of a process formultimodal remediation.

FIG. 16 is a flowchart illustrating one embodiment of a process forscheduling of service provisioning.

FIG. 17 is a flowchart illustrating one embodiment of a process forautomatic customized output generation delivery.

FIG. 18 is a swim lane diagram of one embodiment of a process forautomatic customized output generation delivery.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides preferred exemplary embodiment(s) only,and is not intended to limit the scope, applicability or configurationof the disclosure. Rather, the ensuing description of the preferredexemplary embodiment(s) will provide those skilled in the art with anenabling description for implementing a preferred exemplary embodiment.It is understood that various changes may be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

Some aspects of the present disclosure relate to new methods, systems,and/or devices for gathering information from multiple sources relatingto a single user. This information identifies attributes of the users,each of which corresponds to a dimension in an n-dimensional space. Someor all of these dimensions can be orthogonal. These dimensions canrelate to, for example, one or several physical attributes of the user,one or several mental attributes of the user, one or several socialattributes of the user, one or several spiritual attributes of the user,one or several socio-economic attributes of the user, or the like. Theseattributes can be aggregated and conformed to a desired and/or commonformat. These attributes can then be used to form a vector that overallcharacterizes the user. This vector can comprise a magnitude and adirection. In some embodiments, one or both of the magnitude and thedirection of the vector can be associated with an overall evaluation ofthe user, and changes to the magnitude and/or direction of the vectorcan correspond to a change in the overall evaluation of the user. Insome embodiments, the magnitude and/or direction of the vector can beused to identify one or several interventions for the user, a risk levelfor the user, or the like.

In some embodiments, the attributes of the user can be tracked overtime, and the vector associated with the user can be tracked over time.Changes to the attributes and/or to the vector can be indicative of achange to the status of the user such as, for example, a change to thehealth and/or well-being of the user.

In some aspects, the present disclosure relates to a networked databasecomprising a hybrid network formed of a plurality of n-number ofnetworks, each of which networks comprising a plurality of nodes. Insome embodiments, this can include a first network comprising nodes thateach correspond one or several user states, a second network comprisingnodes that each correspond to one or several user characteristics, athird network comprising nodes that each correspond to one or severalremedial actions, a fourth network comprising nodes that each correspondto one or several outcomes. In some embodiments, nodes from theplurality of networks can be interlinked via a plurality of edges,wherein each edge connects a pair of nodes. Each of these edges can beassociated with a conditional probability that can characterize theprobability of the truth of one of the nodes in the pair of nodes linkedby the edge based on the status of the other of the nodes in the pair ofnodes linked by the edge.

In some aspects, these networks can be customizable based on one orseveral attributes of the user that can be stored in a user profile.These attributes of the user can be the same attributes as those used toform the vector, can be different attributes than those used to form thevector, or can be partially the same attributes as those used to formthe vector. In some embodiments, conditional probabilities associatedwith edges connecting nodes can vary based on these attributes. Forexample, an attribute may indicate a strong user preference for oragainst a type of action. This attribute may thus impact conditionalprobabilities associated with edges linking nodes related to thataction.

The hybrid network can be used to customize interventions provided tothe user. This can include customization according to efficacy,according to a cost function, according to user preference, or the like.In some embodiments, this customization can be made based on theexpected outcome for a user having the attributes of the current user,similarly, in some embodiments this cost function can be a customizedcost function tailored to a user having the attributes of the user.

Some aspects of the present disclosure relate to the autogeneration ofdata, text, and/or messages based on a plurality of inputs. These inputscan include one or several inputs provided by the apparent author of thetext and one or several inputs provided by the recipient of the data,text, and/or messages. Thus, the autogeneration of the data, text,and/or messages can be dependent on inputs by both the apparent creatorof the text as well as by the recipient of the data, text, and/ormessages. In some embodiments, based on a single set of inputs by theapparent author of the data, text, and/or messages, a plurality of data,text, and/or messages can be outputted, each corresponding to one orseveral inputs received from one or several recipients.

With reference now to FIG. 1, a block diagram is shown illustratingvarious components of an automated provisioning network 10 whichimplements and supports certain embodiments and features describedherein. In some embodiments, the automated provisioning network 10 cancomprise one or several physical components and/or one or severalvirtual components such as, for example, one or several cloud computingcomponents. In some embodiments, the automated provisioning network 10can comprise a mixture of physical and cloud computing components.

Automated provisioning network 10 may include one or more main servers101. As discussed below in more detail, main servers 101 may be anydesired type of server including, for example, a rack server, a towerserver, a miniature server, a blade server, a mini rack server, a mobileserver, an ultra-dense server, a super server, or the like, and mayinclude various hardware components, for example, a motherboard, aprocessing unit, memory systems, hard drives, network interfaces, powersupplies, etc. Main server 101 may include one or more server farms,clusters, or any other appropriate arrangement and/or combination orcomputer servers. Main server 101 may act according to storedinstructions located in a memory subsystem of the server 101, and mayrun an operating system, including any commercially available serveroperating system and/or any other operating systems discussed herein.

The automated provisioning network 10 may include one or more data storeservers 14, such as database servers and file-based storage systems. Thedatabase servers 14 can access data that can be stored on a variety ofhardware components. These hardware components can include, for example,components forming tier 0 storage, components forming tier 1 storage,components forming tier 2 storage, and/or any other tier of storage. Insome embodiments, tier 0 storage refers to storage that is the fastesttier of storage in the database server 14, and particularly, the tier 0storage is the fastest storage that is not RAM or cache memory. In someembodiments, the tier 0 memory can be embodied in solid state memorysuch as, for example, a solid-state drive (SSD) and/or flash memory.

In some embodiments, the tier 1 storage refers to storage that is one orseveral higher performing systems in the memory management system, andthat is relatively slower than tier 0 memory, and relatively faster thanother tiers of memory. The tier 1 memory can be one or several harddisks that can be, for example, high-performance hard disks. These harddisks can be one or both of physically or communicatively connected suchas, for example, by one or several fiber channels. In some embodiments,the one or several disks can be arranged into a disk storage system, andspecifically can be arranged into an enterprise class disk storagesystem. The disk storage system can include any desired level ofredundancy to protect data stored therein, and in one embodiment, thedisk storage system can be made with grid architecture that createsparallelism for uniform allocation of system resources and balanced datadistribution.

In some embodiments, the tier 2 storage refers to storage that includesone or several relatively lower performing systems in the memorymanagement system, as compared to the tier 1 and tier 2 storages. Thus,tier 2 memory is relatively slower than tier 1 and tier 0 memories. Tier2 memory can include one or several SATA-drives (e.g., Serial ATAttachment drives) or one or several NL-SATA drives.

In some embodiments, the one or several hardware and/or softwarecomponents of the database server 14 can be arranged into one or severalstorage area networks (SAN), which one or several storage area networkscan be one or several dedicated networks that provide access to datastorage, and particularly that provides access to consolidated, blocklevel data storage. A SAN typically has its own network of storagedevices that are generally not accessible through the local area network(LAN) by other devices. The SAN allows access to these devices in amanner such that these devices appear to be locally attached to the userdevice.

Data stores 14 may comprise stored data relevant to the functions of theautomated provisioning network 10. Illustrative examples of data stores14 that may be maintained in certain embodiments of the automatedprovisioning network 10 are described throughout the application. Insome embodiments, multiple data stores may reside on a single server 14,either using the same storage components of server 14 or using differentphysical storage components to assure data security and integritybetween data stores. In other embodiments, each data store may have aseparate dedicated data store server 14.

Automated provisioning network 10 also may include one or more providerdevices 136, also referred to herein as source devices 136, user devices140, recipient devices 141, and/or analyst devices 148. Source devices136, user devices 140, recipient devices 141, and analyst devices 148may display content received via the automated provisioning network 10,and may support various types of user interactions with the content.Source devices 136, user devices 140, recipient devices 141, and analystdevices 148 may include mobile devices such as smartphones, tabletcomputers, personal digital assistants, and wearable computing devices.Such mobile devices may run a variety of mobile operating systems andmay be enabled for Internet, e-mail, short message service (SMS),Bluetooth®, mobile radio-frequency identification (M-RFID), and/or othercommunication protocols. Other source devices 136, user devices 140,recipient devices 141, and analyst devices 148 may be general purposepersonal computers or special-purpose computing devices including, byway of example, personal computers, laptop computers, workstationcomputers, projection devices, and interactive room display systems.Additionally, source devices 136, user devices 140, recipient devices141, and analyst devices 148 may be any other electronic devices, suchas a thin-client computers, an Internet-enabled gaming systems, businessor home appliances, and/or a personal messaging devices, capable ofcommunicating over network(s) 22.

In different contexts of automated provisioning networks 10, sourcedevices 136, user devices 140, recipient devices 141, and analystdevices 148 may correspond to different types of specialized devices,for example, clinician devices, patient devices and analyst devices inan care provisioning network and/or care management network, employeedevices and presentation devices in a company network, different gamingdevices in a gaming network, etc. In some embodiments, source devices136, user devices 140, recipient devices 141, and analyst devices 148may operate in the same physical location, such as in a clinic, examroom, surgical room, etc. In such cases, the devices may containcomponents that support direct communications with other nearby devices,such as wireless transceivers and wireless communications interfaces,Ethernet sockets or other Local Area Network (LAN) interfaces, etc. Inother implementations, the source devices 136, user devices 140,recipient devices 141, and analyst devices 148 need not be used at thesame location, but may be used in remote geographic locations in whicheach source device 136, user device 140, recipient devices 141, andanalyst device 148 may use security features and/or specialized hardware(e.g., hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.)to communicate with the main server 101 and/or other remotely locateddevices 136, 140, 141, 148. Additionally, different source devices 136,user devices 140, recipient devices 141, and analyst devices 148 may beassigned different designated roles, such as presenter devices, userdevices, source devices, analyst devices, or the like, and in such casesthe different devices may be provided with additional hardware and/orsoftware components to provide content and support user capabilities notavailable to the other devices.

The automated provisioning network 10 also may include a privacy server18 that maintains private user information at the privacy server 18while using applications or services hosted on other servers. Forexample, the privacy server 18 may be used to maintain private data of auser within one jurisdiction even though the user is accessing anapplication hosted on a server (e.g., the main server 101) locatedoutside the jurisdiction. In such cases, the privacy server 18 mayintercept communications between a source device 136, a user device 140,and/or analyst device 148 and other devices that include private userinformation. The privacy server 18 may create a token or identifier thatdoes not disclose the private information and may use the token oridentifier when communicating with the other servers and systems,instead of using the user's private information.

As illustrated in FIG. 1, the main server 101 may be in communicationwith one or more additional servers, such as a content server 12, a userdata server 20, and/or an administrator server 16. Each of these serversmay include some or all of the same physical and logical components asthe main server(s) 101, and in some cases, the hardware and softwarecomponents of these servers 12, 16, 20 may be incorporated into the mainserver(s) 101, rather than being implemented as separate computerservers.

Content server 12 may include hardware and software components togenerate, store, and maintain the content resources for distribution touser devices 140 and other devices in the network 10. For example, inautomated provisioning networks 10 used for professional training andeducational purposes, content server 12 may include data stores oftraining materials, presentations, plans, syllabi, reviews, evaluations,interactive programs and simulations, course models, course outlines,and various training interfaces that correspond to different materialsand/or different types of user devices 140. In automated provisioningnetworks 10 used for media distribution, interactive gaming, and thelike, a content server 12 may include media content files such as music,movies, television programming, games, and advertisements.

User data server 20 may include hardware and software components thatstore and process data for multiple users relating to each user'sactivities and usage of the automated provisioning network 10. Forexample, the main server 101 may record and track each user's systemusage, including their source device 136, user device 140, and/oranalyst device 148, content resources accessed, and interactions withother source devices 136, user devices 140, and/or analyst devices 148.This data may be stored and processed by the user data server 20, tosupport user tracking and analysis features. For instance, in theprofessional training and educational contexts, the user data server 20may store and analyze each user's training materials viewed,presentations attended, courses completed, interactions, evaluationresults, and the like. The user data server 20 may also include arepository for user-generated material, such as evaluations and testscompleted by users, and documents and assignments prepared by users. Insome embodiments, the user data server 20 can further store informationrelating to a user's certificates, certifications, qualifications,completed trainings, licensing, licensed jurisdictions, or the like. Insome embodiments, the user data server 20 can further store informationrelating to the status of the user's certificates, certifications,qualifications, completed trainings, and/or licensing. This can includewhether and/or when the user's certificates, certifications,qualifications, completed trainings, and/or licensings expire and/orlapse. In the context of media distribution and interactive gaming, theuser data server 20 may store and process resource access data formultiple users (e.g., content titles accessed, access times, data usageamounts, gaming histories, user devices and device types, etc.).

Administrator server 16 may include hardware and software components toinitiate various administrative functions at the main server 101 andother components within the automated provisioning network 10. Forexample, the administrator server 16 may monitor device status andperformance for the various servers, data stores, and/or user devices136, 140, 141, 148 in the automated provisioning network 10. Whennecessary, the administrator server 16 may add or remove devices fromthe network 10, and perform device maintenance such as providingsoftware updates to the devices in the network 10. Variousadministrative tools on the administrator server 16 may allow authorizedusers to set user access permissions to various content resources,monitor resource usage by users and devices 136, 140, 141, 148, andperform analyses and generate reports on specific network users and/ordevices (e.g., resource usage tracking reports, training evaluations,etc.).

The automated provisioning network 10 may include one or morecommunication networks 22. Although only a single network 22 isidentified in FIG. 1, the automated provisioning network 10 may includeany number of different communication networks between any of thecomputer servers and devices shown in FIG. 1 and/or other devicesdescribed herein. Communication networks 22 may enable communicationbetween the various computing devices, servers, and other components ofthe automated provisioning network 10. As discussed below, variousimplementations of automated provisioning networks 10 may employdifferent types of networks 22, for example, computer networks,telecommunications networks, wireless networks, and/or any combinationof these and/or other networks.

The automated provisioning network 10 may include one or severalnavigation systems or features including, for example, the GlobalPositioning System (“GPS”), GALILEO (e.g., Europe's global positioningsystem), or the like, or location systems or features including, forexample, one or several transceivers that can determine location of theone or several components of the automated provisioning network 10 via,for example, triangulation. All of these are depicted as navigationsystem 24.

In some embodiments, navigation system 24 can include or severalfeatures that can communicate with one or several components of theautomated provisioning network 10 including, for example, with one orseveral of the source devices 136, with one or several of the userdevices 140, and/or with one or several of the analyst devices 148. Insome embodiments, this communication can include the transmission of asignal from the navigation system 24 which signal is received by one orseveral components of the automated provisioning network 10 and can beused to determine the location of the one or several components of theautomated provisioning network 10.

With reference to FIG. 2, an illustrative distributed computingenvironment 30 is shown including a computer server 32, four clientcomputing devices 36, and other components that may implement certainembodiments and features described herein. In some embodiments, theserver 32 may correspond to the main server 101 discussed above in FIG.1, and the client computing devices 36 may correspond to one or more ofthe source devices 136, the user devices 140, recipient devices 141,and/or the analyst devices 148. However, the computing environment 30illustrated in FIG. 2 may correspond to any other combination of devicesand servers configured to implement a client-server model or otherdistributed computing architecture.

Client devices 36 may be configured to receive and execute clientapplications over one or more networks 44. Such client applications maybe web browser based applications and/or standalone softwareapplications, such as mobile device applications. Server 32 may becommunicatively coupled with the client devices 36 via one or morecommunication networks 44. Client devices 36 may receive clientapplications from server 32 or from other application providers (e.g.,public or private application stores). Server 32 may be configured torun one or more server software applications or services, for example,web-based or cloud-based services, to support content distribution andinteraction with client devices 36. Users operating client devices 36may in turn utilize one or more client applications (e.g., virtualclient applications) to interact with server 32 to utilize the servicesprovided by these components.

Various different subsystems and/or components 34 may be implemented onserver 32. Users operating the client devices 36 may initiate one ormore client applications to use services provided by these subsystemsand components. The subsystems and components within the server 32 andclient devices 36 may be implemented in hardware, firmware, software, orcombinations thereof. Various different system configurations arepossible in different distributed computing systems 30 and automatedprovisioning networks 10. The embodiment shown in FIG. 2 is thus oneexample of a distributed computing system and is not intended to belimiting.

Although exemplary computing environment 30 is shown with four clientcomputing devices 36, any number of client computing devices may besupported. Other devices, such as specialized sensor devices, etc., mayinteract with client devices 36 and/or server 32.

As shown in FIG. 2, various security and integration components 38 maybe used to send and manage communications between the server 32 and userdevices 36 over one or more communication networks 44. The security andintegration components 38 may include separate servers, such as webservers and/or authentication servers, and/or specialized networkingcomponents, such as firewalls, routers, gateways, load balancers, andthe like. In some cases, the security and integration components 38 maycorrespond to a set of dedicated hardware and/or software operating atthe same physical location and under the control of the same entities asserver 32. For example, components 38 may include one or more dedicatedweb servers and network hardware in a datacenter or a cloudinfrastructure. In other examples, the security and integrationcomponents 38 may correspond to separate hardware and softwarecomponents which may be operated at a separate physical location and/orby a separate entity.

Security and integration components 38 may implement various securityfeatures for data transmission and storage, such as authenticating usersand restricting access to unknown or unauthorized users. In variousimplementations, security and integration components 38 may provide, forexample, a file-based integration scheme or a service-based integrationscheme for transmitting data between the various devices in theautomated provisioning network 10. Security and integration components38 also may use secure data transmission protocols and/or encryption fordata transfers, for example, File Transfer Protocol (FTP), Secure FileTransfer Protocol (SFTP), and/or Pretty Good Privacy (PGP) encryption.

In some embodiments, one or more web services may be implemented withinthe security and integration components 38 and/or elsewhere within theautomated provisioning network 10. Such web services, includingcross-domain and/or cross-platform web services, may be developed forenterprise use in accordance with various web service standards, such asRESTful web services (i.e., services based on the Representation StateTransfer (REST) architectural style and constraints), and/or webservices designed in accordance with the Web Service Interoperability(WS-I) guidelines. Some web services may use the Secure Sockets Layer(SSL) or Transport Layer Security (TLS) protocol to provide secureconnections between the server 32 and user devices 36. SSL or TLS mayuse HTTP or HTTPS to provide authentication and confidentiality. Inother examples, web services may be implemented using REST over HTTPSwith the OAuth open standard for authentication, or using theWS-Security standard which provides for secure SOAP (e.g., Simple ObjectAccess Protocol) messages using Extensible Markup Language (XML)encryption. In other examples, the security and integration components38 may include specialized hardware for providing secure web services.For example, security and integration components 38 may include securenetwork appliances having built-in features such as hardware-acceleratedSSL and HTTPS, WS-Security, and firewalls. Such specialized hardware maybe installed and configured in front of any web servers, so that anyexternal devices may communicate directly with the specialized hardware.

Communication network(s) 44 may be any type of network familiar to thoseskilled in the art that can support data communications using any of avariety of commercially-available protocols, including withoutlimitation, TCP/IP (transmission control protocol/Internet protocol),SNA (systems network architecture), IPX (Internet packet exchange),Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols,Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text TransferProtocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and thelike. Merely by way of example, network(s) 44 may be local area networks(LAN), such as one based on Ethernet, Token-Ring, and/or the like.Network(s) 44 also may be wide-area networks, such as the Internet.Networks 44 may include telecommunication networks such as a publicswitched telephone networks (PSTNs), or virtual networks such as anintranet or an extranet. Infrared and wireless networks (e.g., using theInstitute of Electrical and Electronics (IEEE) 802.11 protocol suite orother wireless protocols) also may be included in networks 44.

Computing environment 30 also may include one or more data stores 40and/or back-end servers 42. In certain examples, the data stores 40 maycorrespond to data store server(s) 14 discussed above in FIG. 1, andback-end servers 42 may correspond to the various back-end servers 12,14, 16, 20. Data stores 40 and servers 42 may reside in the samedatacenter or may operate at a remote location from server 32. In somecases, one or more data stores 40 may reside on a non-transitory storagemedium within the server 32. Other data stores 40 and back-end servers42 may be remote from server 32 and configured to communicate withserver 32 via one or more networks 44. In certain embodiments, datastores 40 and back-end servers 42 may reside in a storage-area network(SAN), or may use storage-as-a-service (STaaS) architectural model.

With reference now to FIG. 3, a block diagram is shown illustrating anembodiment of one or more main servers 101 within an automatedprovisioning network 10. In such an embodiment, main server 101 performsinternal data gathering and processing of streamed content along withexternal data gathering and processing. Other embodiments could haveeither all external or all internal data gathering. This embodimentallows reporting timely information that might be of interest to thereporting party or other parties. In this embodiment, the main server101 can monitor gathered information from several sources to allow it tomake timely business and/or processing decisions based upon thatinformation. For example, reports of user actions and/or responses, aswell as the status and/or results of one or several processing taskscould be gathered and reported to the main server 101 from a number ofsources.

Internally, the main server 101 gathers information from one or moreinternal components 48-54. The internal components 48-54 gather and/orprocess information relating to such things as: services provided topatient-users, also referred to herein as subject-users; data gatheredfrom patient-users; inputs from provider-users; inputs provided byrecipient-users, patient-user state; patient-user characteristics;patient-user progress; etc. The internal components 48-54 can report thegathered and/or generated information in real-time, near real-time oralong another time line. To account for any delay in reportinginformation, a time stamp or staleness indicator can inform others ofhow timely the information was sampled. The main server 101 can opt toallow third parties to use internally or externally gathered informationthat is aggregated within the server 101 by subscription to theautomated provisioning network 10.

A command and control (CC) interface 58 configures the gathered inputinformation to an output of data streams, also referred to herein ascontent streams. APIs for accepting gathered information and providingdata streams are provided to third parties external to the server 101who want to subscribe to data streams. The server 101 or a third partycan design as yet undefined APIs using the CC interface 58. The server101 can also define authorization and authentication parameters usingthe CC interface 58 such as authentication, authorization, login, and/ordata encryption. CC information is passed to the internal components48-54 and/or other components of the automated provisioning network 10through a channel separate from the gathered information or data streamin this embodiment, but other embodiments could embed CC information inthese communication channels. The CC information allows throttlinginformation reporting frequency, specifying formats for information anddata streams, deactivation of one or several internal components 48-54and/or other components of the automated provisioning network 10,updating authentication and authorization, etc.

The various data streams that are available can be researched andexplored through the CC interface 58. Those data stream selections for aparticular subscriber, which can be one or several of the internalcomponents 48-54 and/or other components of the automated provisioningnetwork 10, are stored in the queue subscription information database60. The server 101 and/or the CC interface 58 then routes selected datastreams to processing subscribers that have selected delivery of a givendata stream. Additionally, the server 101 also supports historicalqueries of the various data streams that are stored in a historical datastore 62 as gathered by an archive data agent 64. Through the CCinterface 58 various data streams can be selected for archiving into thehistorical data store 62.

Components of the automated provisioning network 10 outside of theserver 101 can also gather information that is reported to the server101 in real-time, near real-time, or along another time line. There is adefined API between those components and the server 101. Each type ofinformation or variable collected by server 101 falls within a definedAPI or multiple APIs. In some cases, the CC interface 58 is used todefine additional variables to modify an API that might be of use toprocessing subscribers. The additional variables can be passed to allprocessing subscribers or just a subset of the processing subscribers.For example, a component of the automated provisioning network 10outside of the server 101 may report a user response, but define anidentifier of that user as a private variable that would not be passedto processing subscribers lacking access to that user and/orauthorization to receive that user data. Processing subscribers havingaccess to that user and/or authorization to receive that user data wouldreceive the subscriber identifier along with the response reported tothat component. Encryption and/or unique addressing of data streams orsub-streams can be used to hide the private variables within themessaging queues.

The source devices 136, user devices 140, and/or analyst devices 148communicate with the server 101 through security and/or integrationhardware 66. The communication with security and/or integration hardware66 can be encrypted or not. For example, a socket using a TCP connectioncould be used. In addition to TCP, other transport layer protocols likeControl Transmission Protocol (SCTP) and User Datagram Protocol (UDP)could be used in some embodiments to intake the gathered information. Aprotocol such as SSL could be used to protect the information over theTCP connection. Authentication and authorization can be performed to anyuser devices 140 and/or supervisor device interfacing to the server 101.The security and/or integration hardware 66 receives the informationfrom one or several of the user devices 140 and/or the analyst devices148 by providing the API and any encryption, authorization, and/orauthentication. In some cases, the security and/or integration hardware66 reformats or rearranges this received information

The messaging bus 56, also referred to herein as a messaging queue or amessaging channel, can receive information from the internal componentsof the server 101 and/or components of the automated provisioningnetwork 10 outside of the server 101 and distribute the gatheredinformation as a data stream to any processing subscribers that haverequested the data stream from the messaging queue 56. As indicated inFIG. 3, processing subscribers are indicated by a connector to themessaging bus 56, the connector having an arrow head pointing away fromthe messaging bus 56. In some examples, only data streams within themessaging queue 56 that a particular processing subscriber hassubscribed to may be read by that processing subscriber if received atall. Gathered information sent to the messaging queue 56 is processedand returned in a data stream in a fraction of a second by the messagingqueue 56. Various multicasting and routing techniques can be used todistribute a data stream from the messaging queue 56 that a number ofprocessing subscribers have requested. Protocols such as Multicast ormultiple Unicast could be used to distributed streams within themessaging queue 56. Additionally, transport layer protocols like TCP,SCTP and UDP could be used in various embodiments.

Through the CC interface 58, an external or internal processingsubscriber can be assigned one or more data streams within the messagingqueue 56. A data stream is a particular type of messages in a particularcategory. For example, a data stream can comprise all of the datareported to the messaging bus 56 by a designated set of components. Oneor more processing subscribers could subscribe and receive the datastream to process the information and make a decision and/or feed theoutput from the processing as gathered information fed back into themessaging queue 56. Through the CC interface 58 a developer can searchthe available data streams or specify a new data stream and its API. Thenew data stream might be determined by processing a number of existingdata streams with a processing subscriber.

The automated provisioning network 10 has internal processingsubscribers 48-54 that process assigned data streams to performfunctions within the server 101. Internal processing subscribers 48-54could perform functions such as: identifying and/or tracking servicesprovided to patient-users; gathering data from patient-users; gatheringdata from provider-users; identifying and/or tracking patient-userstate; identifying and/or tracking one or several patient-usercharacteristics; identifying and/or tracking patient-user progress;recommending services and/or user actions; or the like. The internalprocessing subscribers 48-54 can decide filtering and weighting ofrecords from the data stream. To the extent that decisions are madebased upon analysis of the data stream, each data record is time stampedto reflect when the information was gathered such that additionalcredibility could be given to more recent results, for example. Otherembodiments may filter out records in the data stream that are from anunreliable source or stale. For example, a particular contributor ofinformation may prove to have less than optimal gathered information andthat could be weighted very low or removed altogether.

Internal processing subscribers 48-54 may additionally process one ormore data streams to provide different information to feed back into themessaging queue 56 to be part of a different data stream. For example,hundreds of user devices 140 could provide responses that are put into adata stream on the messaging queue 56. An internal processing subscriber48-54 could receive the data stream and process it to determine thedifficulty of one or several data packets provided to one or severalusers and supply this information back onto the messaging queue 56 forpossible use by other internal and external processing subscribers.

As mentioned above, the CC interface 58 allows the automatedprovisioning network 10 to query historical messaging queue 56information. An archive data agent 64 listens to the messaging queue 56to store data streams in a historical database 62. The historicaldatabase 62 may store data streams for varying amounts of time and maynot store all data streams. Different data streams may be stored fordifferent amounts of time.

With regard to the components 48-54, the main server(s) 101 may includevarious server hardware and software components that manage the contentresources within the automated provisioning network 10 and provideinteractive and adaptive content to users on various user devices 140.For example, main server(s) 101 may provide instructions to and receiveinformation from the other devices within the automated provisioningnetwork 10, in order to manage and transmit content resources, userdata, and server or client applications executing within the network 10.

With reference now to FIG. 4, a block diagram of an illustrativecomputer system is shown. The system 70 may correspond to any of thecomputing devices or servers of the automated provisioning network 10described above, or any other computing devices described herein, andspecifically can include, for example, one or several of the sourcedevices 136, one or several of the user devices 140, and/or one orseveral of the analyst device 148, and/or any of the servers 101, 12,14, 16, 18, 20. In this example, computer system 70 includes processingunits 72 that communicate with a number of peripheral subsystems via abus subsystem 71. These peripheral subsystems include, for example, astorage subsystem 75, an I/O subsystem 83, and a communicationssubsystem 86.

Bus subsystem 71 provides a mechanism for letting the various componentsand subsystems of computer system 70 communicate with each other asintended. Although bus subsystem 71 is shown schematically as a singlebus, alternative embodiments of the bus subsystem may utilize multiplebuses. Bus subsystem 71 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. Sucharchitectures may include, for example, an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 72, which may be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 70. One or more processors,including single core and/or multicore processors, may be included inprocessing unit 72. As shown in the figure, processing unit 72 may beimplemented as one or more independent processing units 73 and/or 74with single or multicore processors and processor caches included ineach processing unit. In other embodiments, processing unit 72 may alsobe implemented as a quad-core processing unit or larger multicoredesigns (e.g., hexa-core processors, octo-core processors, ten-coreprocessors, or greater.

Processing unit 72 may execute a variety of software processes embodiedin program code, and may maintain multiple concurrently executingprograms or processes. At any given time, some or all of the programcode to be executed can be resident in processor(s) 72 and/or in storagesubsystem 75. In some embodiments, computer system 70 may include one ormore specialized processors, such as digital signal processors (DSPs),outboard processors, graphics processors, application-specificprocessors, and/or the like.

I/O subsystem 83 may include device controllers 84 for one or more userinterface input devices and/or user interface output devices 85. Userinterface input and output devices 85 may be integral with the computersystem 70 (e.g., integrated audio/video systems, and/or touchscreendisplays), or may be separate peripheral devices which areattachable/detachable from the computer system 70. The I/O subsystem 83may provide one or several outputs to a user by converting one orseveral electrical signals to user perceptible and/or interpretableform, and may receive one or several inputs from the user by generatingone or several electrical signals based on one or several user-causedinteractions with the I/O subsystem such as the depressing of a key orbutton, the moving of a mouse, the interaction with a touchscreen ortrackpad, the interaction of a sound wave with a microphone, or thelike.

Input devices 85 may include a keyboard, pointing devices such as amouse or trackball, a touchpad or touch screen incorporated into adisplay, a scroll wheel, a click wheel, a dial, a button, a switch, akeypad, audio input devices with voice command recognition systems,microphones, and other types of input devices. Input devices 85 may alsoinclude three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader 3D scanners, 3Dprinters, laser rangefinders, and eye gaze tracking devices. Additionalinput devices 85 may include, for example, motion sensing and/or gesturerecognition devices that enable users to control and interact with aninput device through a natural user interface using gestures and spokencommands, eye gesture recognition devices that detect eye activity fromusers and transform the eye gestures as input into an input device,voice recognition sensing devices that enable users to interact withvoice recognition systems through voice commands, medical imaging inputdevices, MIDI keyboards, digital musical instruments, and the like.

Output devices 85 may include one or more display subsystems, indicatorlights, or non-visual displays such as audio output devices, etc.Display subsystems may include, for example, cathode ray tube (CRT)displays, flat-panel devices, such as those using a liquid crystaldisplay (LCD) or plasma display, light-emitting diode (LED) displays,projection devices, touch screens, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system70 to a user or other computer. For example, output devices 85 mayinclude, without limitation, a variety of display devices that visuallyconvey text, graphics, and audio/video information such as monitors,printers, speakers, headphones, automotive navigation systems, plotters,voice output devices, and modems.

Computer system 70 may comprise one or more storage subsystems 75,comprising hardware and software components used for storing data andprogram instructions, such as system memory 79 and computer-readablestorage media 78. The system memory 79 and/or computer-readable storagemedia 78 may store program instructions that are loadable and executableon processing units 72, as well as data generated during the executionof these programs.

Depending on the configuration and type of computer system 70, systemmemory 79 may be stored in volatile memory (such as random access memory(RAM) 76) and/or in non-volatile storage drives 77 (such as read-onlymemory (ROM), flash memory, etc.). The RAM 76 may contain data and/orprogram modules that are immediately accessible to and/or presentlybeing operated and executed by processing units 72. In someimplementations, system memory 79 may include multiple different typesof memory, such as static random access memory (SRAM) or dynamic randomaccess memory (DRAM). In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 70, such as duringstart-up, may typically be stored in the non-volatile storage drives 77.By way of example, and not limitation, system memory 79 may includeapplication programs 80, such as client applications, Web browsers,mid-tier applications, server applications, etc., program data 81, andan operating system 82.

Storage subsystem 75 also may provide one or more tangiblecomputer-readable storage media 78 for storing the basic programming anddata constructs that provide the functionality of some embodiments.Software (programs, code modules, instructions) that when executed by aprocessor provide the functionality described herein may be stored instorage subsystem 75. These software modules or instructions may beexecuted by processing units 72. Storage subsystem 75 may also provide arepository for storing data used in accordance with the presentinvention.

Storage subsystem 75 may also include a computer-readable storage mediareader that can further be connected to computer-readable storage media78. Together and, optionally, in combination with system memory 79,computer-readable storage media 78 may comprehensively represent remote,local, fixed, and/or removable storage devices plus storage media fortemporarily and/or more permanently containing, storing, transmitting,and retrieving computer-readable information.

Computer-readable storage media 78 containing program code, or portionsof program code, may include any appropriate media known or used in theart, including storage media and communication media, such as, but notlimited to, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computer system 70.

By way of example, computer-readable storage media 78 may include a harddisk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 78 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 78 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 70.

Communications subsystem 86 may provide a communication interface fromcomputer system 70 and external computing devices via one or morecommunication networks, including local area networks (LANs), wide areanetworks (WANs) (e.g., the Internet), and various wirelesstelecommunications networks. As illustrated in FIG. 4, thecommunications subsystem 86 may include, for example, one or morenetwork interface controllers (NICs) 87, such as Ethernet cards,Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as wellas one or more wireless communications interfaces 88, such as wirelessnetwork interface controllers (WNICs), wireless network adapters, andthe like. As illustrated in FIG. 4, the communications subsystem 86 mayinclude, for example, one or more location determining features 89 suchas one or several navigation system features and/or receivers, and thelike. Additionally and/or alternatively, the communications subsystem 86may include one or more modems (telephone, satellite, cable, ISDN),synchronous or asynchronous digital subscriber line (DSL) units,FireWire® interfaces, USB® interfaces, and the like. Communicationssubsystem 88 also may include radio frequency (RF) transceivercomponents for accessing wireless voice and/or data networks (e.g.,using cellular telephone technology, advanced data network technology,such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi(IEEE 802.11 family standards, or other mobile communicationtechnologies, or any combination thereof), global positioning system(GPS) receiver components, and/or other components.

The various physical components of the communications subsystem 86 maybe detachable components coupled to the computer system 70 via acomputer network, a FireWire® bus, or the like, and/or may be physicallyintegrated onto a motherboard of the computer system 70. Communicationssubsystem 86 also may be implemented in whole or in part by software.

In some embodiments, communications subsystem 86 may also receive inputcommunication in the form of structured and/or unstructured data feeds,event streams, event updates, and the like, on behalf of one or moreusers who may use or access computer system 70. For example,communications subsystem 86 may be configured to receive data feeds inreal-time from users of social networks and/or other communicationservices, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources(e.g., external data source 313). Additionally, communications subsystem86 may be configured to receive data in the form of continuous datastreams, which may include event streams of real-time events and/orevent updates (e.g., sensor data applications, financial tickers,network performance measuring tools, clickstream analysis tools,automobile traffic monitoring, etc.). Communications subsystem 86 mayoutput such structured and/or unstructured data feeds, event streams,event updates, and the like to one or more data stores 14 that may be incommunication with one or more streaming data source computers coupledto computer system 70.

Due to the ever-changing nature of computers and networks, thedescription of computer system 70 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software, or acombination. Further, connection to other computing devices, such asnetwork input/output devices, may be employed. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

Referring initially to FIG. 5, an embodiment of a multimodalprovisioning system 100 is shown that systemically manages user care,also referred to herein as user service provisioning. The multimodalprovisioning system 100 can be embodied in all or portions of theautomated provisioning network 10. In some embodiments, three differentgroups of users interact with the multimodal provisioning system 100,specifically, users, which can be, for example, users under care, alsoreferred to herein as user service recipients or patient-users,providers, also referred to as provider-users, providing the serviceprovisioning, and outcome analysts who gauge recovery and/or theeffectiveness of the provided services in changing the patient-user'scondition or state. Each group of users have their respective sourcedevices 136, user devices 140, and/or analyst devices 148, to interfacewith the multimodal provisioning system 100 that include smartphoneapps, web portals, application software.

The source devices 136 interact with several interfaces, specifically,an output engine 152, provider dashboards 128 and an outcome assessmentinterface 144. The provider dashboards 128 display customized screensthat may be designed by the provider differently for different users,service provisioning lines, groups of users, service provisioningmodules, etc., which are stored as profiles in an interface profiledatabase 156 The progression of user progress, which progress caninclude recovery, can be graphed by user or any group of users, forexample showing the global health composite score, also referred toherein as a user composite characterization. In some embodiments, thiscomposite characterization can be a tensor or vector, and is referred toherein as a summary vector, a user vector and/or acharacterization-vector. In some embodiments, thecharacterization-vector can be created from all or portions of the usercomposite characterization and the characterization-vector can map oneor several scored criterial to an n-dimensional space. In someembodiments, all or portions of the n-dimensional space can correspondto high risk users and/or users requiring complex care. In someembodiments, different locations within the n-dimensional space cancorrespond to different levels of care.

The output engine 152 allows the provider, also referred to herein asthe source, to review content such as, for example, charts in a bespokeand/or summary manner. Different service provisioning modules, usergroups, and users can trigger different profiles that can be stored inthe interface profile database 156 for the output engine 152. Thesedifferent profiles can cause an interface provided by the output engine152 to morph into different interfaces, formats, data display and queryforms according to the context. For example, when a source isinteracting with a particular user, that user's data can be displayedwith personal information relevant for the workflow of serviceprovisioning along with forms for any missing information or informationrequiring verification. The output engine 152 can read back notesassociated with the user with a spoken avatar voice that allows naturallanguage input queries with the note. Each audio note can also becustomized such that the narrative is context aware according to therelevant profile 156.

Users have computers, tablets, smart phones, and wearables as userdevices 140 that can interact with the multimodal provisioning system100. Wearables can be proprietary to the multimodal provisioning system100 or accessed with an API to a third-party system (not shown). In thisembodiment, the wearable user devices 140 interface with the wearablebackend 160 such that activity and vitals can be fed into the multimodalprovisioning system 100 to confirm performance of steps in a serviceprovisioning workflow and vitals during service provisioning. The userdevices 140 can also connect to the outcome assessment interface 144 sothat the user can see their progression and evaluations of their currentstate and/or condition.

The user portal 132 is the primary resource for the users whileinteracting with the multimodal provisioning system 100. Prior to anyinteraction or service provisioning, any desired information can begathered from the user via the user portal 132. This gatheredinformation can be stored for use in connection with a futureinteraction with the user and/or between a source and the user. In someembodiments, electronic forms can accumulate the questions that mayoriginate from a number of service provisioning modules. Some or all ofthese accumulated questions can be presented cohesively withoutrequiring redundant information. Certain questions are aimed to assessemotional and physical wellbeing as service provisioning improves auser's current state and/or condition. The user portal 132 allowschecking-in for a service event such as an appointment and/or printingor generating a badge or barcode for use at the office reception.Scanning of a printed or screen-displayed barcode allows avoiding theconventional form completion processes for a service provisioningappointment. The calendar function on the user device 140 is accessibleto cross-reference availability for scheduling service provisioning.Intelligence on commute times is used to avoid meetings scheduled toclose together to allow for travel given typical traffic patterns at thescheduled time for a proposed appointment. The user portal can furtherfacilitate gathering information such as attributes of one or severalusers, and specifically of one or several recipient-users. In someembodiments, these recipient-users can access the multimodalprovisioning system 100 via one or several recipient devices 141. Theserecipient-users can, for example, be intended recipient of data outputsof the multimodal provisioning system 100, such as, for example, outputscharacterizing one or several attributes of a subject-user, outputscharacterizing one or several attributes or results of services providedto the subject-user, or the like. In some embodiments, therecipient-user can be the subject-user, can be the provider-user, can bean obligated-user such as, for example, a representative of an insurancecompany, or the like.

Outcome analysts gather information to assess progression through theservice provisioning process. In some embodiment, progression throughthe service provisioning process and/or compliance with the regimen isautomatically determined and/or associated data is automaticallygathered. Some clinical test results, also referred to herein asassessment results are automatically gathered along with informationfrom other testing equipment, which testing equipment can includemedical testing equipment. In some embodiments, some or all of thegathered information is available to the analyst devices 148 using theoutcome assessment interface 144. The outcome analyst will often use theanalyst device 148 to manually gather additional information, administertesting and choreograph other information gathering using the outcomeassessment interface.

The outcome assessment interface 144, user portal 132, wearable backend160, provider dashboards 128, and output engine 152 are shown beinghosted in the cloud, but some or all could also be behind-the-firewalland hosted on the other side of the network 120 or a combination of bothhosted and cloud footprints in various embodiments. The internet 124 isconnected to the network 120 across a firewall (not shown).

A multimodal care engine 104, also referred to herein as a multimodalprovisioning engine 104, and user management system 108 interact with acentral server 112 that is coupled to a user profile database 116. Insome embodiments, the user profile database can comprise one or severaluser profiles, which can each comprise n-dimension attributes of one orseveral users. In some embodiments, each user can have a unique userprofile that can include, n-dimension attributes of that user. Theseattributes can be gathered by the multimodal provisioning system 100 andspecifically can be gathered from, for example, the user device 140, thesource device 136, or any other device, or equipment connected to themultimodal provisioning system 100. The central server 112 can receiveinputs from other components of the multimodal provisioning system 100including, for example, the source device 136, the user device 140,and/or any other device, or equipment connected to the multimodalprovisioning system 100. The central server 112 could be a third partyproduct from any number of different vendors. The functionality of themultimodal provisioning system 100 is agnostic to the differentunderlying central servers 112 that might be used. Information on theuser, service provisioning performed, scheduling, regimens assigned,etc. are stored in the user profile database 116 to the extent that thecentral server 112 supports those data fields. Where the additional datais not supported by a particular third-party central server 112, itwould be stored in elsewhere in the multimodal provisioning engine 104and/or the user management system 108.

The multimodal provisioning system 100 can include an action database117. The action database can comprise data relating to one or severalservices for provisioning to the subject-user and/or actions for takingby the subject-user. In some embodiments, the action database 117 cancomprise a multi-dimensional network comprising a plurality of nodesthat can be linked by a plurality of edges. In some embodiments, thismulti-dimensional network can include a first network comprising nodesthat each correspond one or several user states, a second networkcomprising nodes that each correspond to one or several usercharacteristics, a third network comprising nodes that each correspondto one or several remedial actions, a fourth network comprising nodesthat each correspond to one or several outcomes. In some embodiments,nodes from the plurality of networks can be interlinked via a pluralityof edges, wherein each edge connects a pair of nodes. Each of theseedges can be associated with a conditional probability that cancharacterize the probability of the truth of one of the nodes in thepair of nodes linked by the edge based on the status of the other of thenodes in the pair of nodes linked by the edge.

In some aspects, this multi-dimensional network can be customizablebased on one or several attributes of the user that can be stored in auser profile. These attributes of the user can be the same attributes asthose used to for the vector, can be different attributes than thoseused to form the vector, or can be partially the same attributes asthose used to form the vector. In some embodiments, conditionalprobabilities associated with edges connecting nodes can vary based onthese attributes. For example, an attribute may indicate a strong userpreference for or against a type of action. This attribute may thusimpact conditional probabilities associated with edges linking nodesrelated to that action.

Referring next to FIG. 6, an embodiment of a multimodal provisioningengine 104 is shown in detail. In some embodiments, the multimodalprovisioning engine 104 can comprise one of the internal components48-54 of the server 101, and in some embodiments, the components of themultimodal provisioning engine 104 can comprise the internal components48-54 of the server 101. In such an embodiment, the component of themultimodal provisioning engine 104 may be linked by the messaging bus 56and may coordinate actions and/or act based on the data stream receivedfrom the messaging bus 56 according to an event-based design.

A provisioning controller 204 choreographs the operation of themultimodal provisioning engine 104. For each service provisioning linestate model, there is a service provisioning module 212 that has all theinterface elements, testing questions, data fields, workflow the serviceprovisioning line, etc. necessary to control, interact and manage theservice provisioning line. For example, one service provisioning linecould be opioid addiction counseling and the service provisioning modulewould have screens for the source to gather information on currentopioid use, screens to gather survey information from the user directlyor with an outcome analyst, and software to manage user/source/outcomeanalyst workflow as the service provisioning line is complied with bythe user.

The provisioning controller 204 has several interfaces, specifically, anaccess interface 228 to the central server 112 to store userinformation, a PMS interface to the user management system 108 and anassessment interface 224 that retrieves any assessment results, scansand other gathered results, which can include medical results. There areregimen trees 236 stored that are assigned to different user vectors. Auser is tested to determine their user vector at a given point in time.The regimen trees 236 are designed with different service provisioningline state models that are used to improve the health of a user.

Different service locations can support different service provisioninglines such that the corresponding service provisioning modules can beselectively loaded to tailor an instance of the multimodal provisioningengine 104 uniquely for the service location as defined in acorresponding service location model 232. The service location model 232defines the service provisioning lines supported and any specialcustomizations for a particular service location. For example, aparticular service location may support a chiropractic serviceprovisioning module 212 that is customized to have three service sourcessuch as physicians and the service location module would note the threesources to work in parallel such that scheduling is performed in thatmanner.

A service provisioning status system 208 manages scheduling interfaceswith a provider status engine 220 and resource schedule database 216.The provider status system receives schedule information from all thedifferent sources who are available for the service provisioning lines.The actual schedules can be stored on source devices 136 so that othercalendar items can be coordinated. The resource schedule database 216manages equipment, resources, rooms, classes, group sessions, etc.

Prediction models 224 are used to predict a value associated withprovisioned services associated with achievement of a desired outcome,which desired outcome can, in some embodiments, be determined based onone or several attributes of the characterization-vector. Once a uservector is determined and mapped to a regimen tree, the prediction models224 can be used to predict this value. As the outcome analyst updatesprogression along with the automatically gathered information, the uservector is updated along with the value prediction. In some embodiments,service provisioning can involve switching to different regimen treeswhich affects the prediction model. The prediction model 224 can applylearning algorithms to determine how a particular service location willprogress a particular user through various regimen trees to determinevalue to achieve a target user composite characterization.

With reference to FIG. 7, an embodiment of a user management system 108is shown. The user management system 108 uses an access interface 228 toaccess a user profile database 116 and a MCE interface 328 to connectwith the multimodal provisioning engine 104. A user status engine 304manages user appointments and check-in along with assessment andincentives. A user check-in system 308 is coupled to user devices 140 toallow check-in remote from the service location. Surveys, datagathering, testing can be performed with the user device 140 beforearriving at the service location. The user device 140 connects throughthe user portal 132 over the internet 124 to the user management system108. Schedule information from the user device is stored in the userschedules database 320. Reconciliation is done by the user portal 132 atthe time of scheduling or periodically to allow coordination with theservice location.

An assessment system 324 determines compliance and progression of eachuser's wellness. User vectors, summary vectors and global healthcomposites are all stored in the user assessment database 328. Theassessment system 324 processes information gathered from wearables,manually entered by users/outcome analysts/sources, compliance withservice provisioning line state models 332, clinical test results, etc.Generally, the assessment system 324 determines the fields for the uservector and processes that into summary vectors. For example, a field inthe summary vector could be a function of several fields in the uservector. A global health composite is determined from the summary vector.The progress through the user's regimen trees 236 and serviceprovisioning line state models 332 is tracked by the assessment system324.

Incentive module 316 is used in the service provisioning line statemodels to encourage recovery and participation. For some user vectors,offering rewards or other encouragement by the incentive module is usedto improve recovery. This gamification is controlled by the incentivemodule 316 to change the enticement in real time as the user vectorevolves. Service provisioning can be resource intensive and thepredictor models 224 allow estimating of these resources.

Referring next to FIG. 8, an embodiment of a service location model 232is shown for an example service location. Different service provisioninglines 440 are organized into four departments 450, also referred toherein as four service groups 450. In some embodiments these servicegroups are formed based on one or several attributes of the serviceprovisioning lines in the services groups, and in some embodiments,these service groups are formed independent of attributes of serviceproviders provider these service lines. For example, the behavioralservice group 450-1 includes the following service provisioning lines440: psychiatry/psychology, biofeedback, meditation, and guidedhypnosis. Different service locations have different serviceprovisioning lines 440 and departments. Some service provisioning lines440 can be outsourced so long as the corresponding service provisioningmodules 212 are interacted with so that the service provisioning linestate models 332 for each user are maintained such that the assessmentsystem 324 can accurately track progress.

With reference to FIG. 9, an embodiment of the service provisioninglines 440 for a particular regimen 500 example is shown. The user vectorcan be mapped to a track and/or regimen tree that can include thefollowing service provisioning lines 440: meditation, interventionalpain management, chiropractic, and massage. The service provisioninglines 440 are dynamically selected for each user to interact withaccording the service provisioning line state model 332 for each of theservice provisioning lines 440 assigned. Periodically, the regimen 500is rearranged to include different service provisioning lines 440relevant for the evolving condition of a user. For example, after sixweeks the track is reconsidered to possibly change tracks and theservice provisioning lines 440 associated therewith. In variousembodiments, the length of time on a track before reconsidering can be adifferent amount or even change according to the progression of the useror available resources at the service location.

Referring next to FIG. 10, an embodiment of a service location 232 isshown. This service location 610 has fewer service provisioning lines440 than the embodiment of FIG. 4. For a particular regimen 500, serviceprovisioning lines 440 available dictate those assigned. For example, ifa service location did not have biofeedback 440-D3, meditation 440-D2might be substituted instead. The service provisioning line state model332 might be tweaked when there is a substitute to more fully serve asan equivalent.

With reference to FIG. 11, an embodiment of an outcome assessmentinterface 144 is shown. The outcome analyst interacts with the outcomeassessment interface 144 to review the scoring of the various fields ofthe user vector. This embodiment allows the outcome analyst to overridethe scoring according to updated testing, clinical results, etc. Someembodiments allow sources to modify fields of the user vector. In thisembodiment, there are nine fields in user vector that are measured, butother embodiments can have more or less fields.

Referring next to FIG. 12, an embodiment of a summary vector 700 isshown. In this embodiment, the nine fields in user vector are simplifiedinto a four field summary vector 700. The fields of the summary vectorare a function of the fields in the user vector and optionally otherinformation. For example, the fields for addiction risk, pain index andrelapse risk can be summarized as opiate risk 816 in the summary vector700. Various fields from the user vector might be normalized orotherwise processed such that the field in the summary vector 700 is afunction of those value from the user vector. For example, opiate riskmight be addiction risk plus the pain index and relapse risk, but scaledaccording to the dosage of their opiate prescription.

With reference to FIG. 13, an exemplary embodiment of an emotionalversus physical matrix 800. The physical field 804 and emotional field812 from the summary vector 700 are mapped in a three by three matrixaccording to each of those fields being in three intensities,specifically, mild, moderate and severe. Users are grouped in thesecoarse categories to simplify their handling in one of three tracks. Theemotional track is for users assigned to cells 1B, 1C and 2C. Thephysical track is for is for users assigned to cells 2A, 3A and 3B. Forusers in between those two diagnosis, a mixed track lies in the middlewith users assigned to cells 1A, 2B and 3C. When sources are reviewingusers, the assigned cell is shorthand for their condition as the fulluser vector is difficult to remember the relevance of each field.

With reference now to FIG. 14, a flowchart illustrating one embodimentof a process 900 for automated characterization-vector based predictionis shown. The process 900 can be performed by all or portions of theautomated provisioning network 10 including, for example, all orportions of the multimodal provisioning system 100. The process 900begins a block 902 wherein one or several inputs are received. In someembodiments, each of these one or several inputs can be received fromone or several devices 136, 140, 141, 148. In some embodiments, theinputs can be received by the central server 112, and in someembodiments, some or all of these inputs can relate to a singlesubject-user.

After the input has been received, the process 900 proceeds to block904, wherein the user profile is generated, updated, and/or stored basedon the received inputs. In some embodiments, the user profile can begenerated and/or updated by the central server 112 and can be stored inthe user profile database 116. The user profile can relate to the singlesubject-user for whom some or all of the inputs in block 902 werereceived. In some embodiments, the user profile identifies n-dimensionattributes of the single subject-user for whom some or all of the inputsin block 902 were received. In some embodiments, the singlesubject-user, is referred to as a first subject-user.

At block 906, a characterization vector can be generated and/or stored.The characterization vector can be generated by the central server 112from all or portions of the user profile, and in some embodiments, thecharacterization vector can be stored in the user profile database 116.In some embodiments, the characterization vector can comprise aplurality of dimensions, each of which can comprise a gradatedclassification indicator. In one embodiment, for example, thecharacterization vector can comprise four dimensions, each of which fourdimensions can comprise a gradated classification indicator. In oneembodiment, for example, the four dimensions can comprise, a physicaldimension, and emotional dimension, and interaction dimension, and avulnerability dimension.

In some embodiments, the physical dimension can characterize all orportions of the physical state of the subject-user, the emotionaldimension can characterize all or portions of the emotional state of thesubject-user, the interaction dimension can characterize all or portionsof the subject-users social interactions, and the vulnerabilitydimension can characterize all or portions of the subject-users socialvulnerability. In some embodiments, the physical dimension can becharacterized by a gradated classification indicator which can beexpressed as, for example, one of the numerals: 1, 2, or 3, and in someembodiments, the emotional dimension can be characterized by a gradatedclassification indicator which can be expressed as, for example, one ofthe letters: A, B, or C. In some embodiments, the interaction dimensioncan have two sub-dimensions including, interactions with oneself andinteractions with others. The sub dimension relating to interactionswith oneself can attempt to classify motivation levels of thesubject-user, and can be characterized by a gradated classificationindicator which can be expressed as one of the colors: green, yellow, orred. The sub-dimension relating to interactions with others can attemptto classify the subject-user's response to interactions with others andthe sub dimension can be characterized by a gradated classificationindicator which can be expressed as one of the shapes: circle, square,or triangle. The vulnerability dimension can be characterized by agradated classification indicator which can be expressed as one of thelevels: low, medium, or high.

After the characterization vector has been generated, the process 900proceeds to block 908 wherein a service is identified for provisioning.In some embodiments, the service for provisioning can be identifiedaccording to an artificial intelligence (AI) machine-learning model. Insome embodiments, this machine-learning model can be trained to output aservice for provisioning and/or a service intensity based on the inputof the characterization vector, based on the input of portions of thecharacterization vector, and/or based on the input of portions of theuser profile. In some embodiments, and as a part of the identifying ofthe service for provisioning, the server 112 can be configured to ingestat least a portion of the characterization vector into the AImachine-learning model.

In some embodiments, the service identified for provisioning cancomprise a digital component and a non-digital component. In someembodiments, the service for provisioning can be identified according tothe characterization vector including, for example, according to all orportions of the magnitude, and/or direction of the characterizationvector. In some embodiments, all or portions of the characterizationvector can be linked to one or several services or actions in the actiondatabase 117. In some embodiments, this link can be to one or severalservices or actions can be via edges and corresponding conditionalprobabilities characterizing the strength of the link with thoseservices, and/or actions. In some embodiments, the digital component,can comprise content for delivery to the subject-user. This content cancomprise one or several videos, messages, texts, text strings, emails,or the like. In some embodiments, the digital component can comprise oneor several reminders, one or several calendar entries, one or severalvideos, or the like. In some embodiments, the non-digital component, cancomprise one or several tests, clinic visits, scans, evaluations,therapies, surgeries, treatments, medications, or the like. In someembodiments, the service for provisioning can be identified by queryingthe action database 117 for a service based on attributes of the user.In some embodiments, these attributes can be embodied by thecharacterization vector. In response to this request, the actiondatabase 117 can provide identification of one or several services oractions.

At block 910, a delivery criteria is identified and the fulfillmentstatus of that delivery criteria is determined. In some embodiments, forexample, the delivery criteria can identify one or several parametersconstraining delivery of the identified service. These can be, forexample, one or several times, or location parameters, which time, orlocation parameters can specify a timeframe in which the service can bedelivered and/or a location in which the service can be delivered. Insome embodiments, the delivery criteria can relate to the digitalcomponent of the service and/or to the non-digital component of theservice. In one embodiment, for example, the delivery criteria cancomprise one or several conditions for fulfillment before the digitalcomponent of the service can be provided. These conditions can include,for example, an amount of elapsed time since the receipt of the input inblock 902. In some embodiments, the determination of fulfillment of thedelivery criteria can be made based on information and/or data containedin the data stream outputted by the messaging bus 56. In someembodiments, for example, data contained in the data stream receivedfrom the messaging bus 56 can indicate the completion of an eventcorresponding to the fulfillment of the delivery criteria.

At block 912, the service identified in block 908 is delivered. In someembodiments, the delivery of this service can include the automaticdelivery of the digital component of the service subsequent to theautomated determination of the fulfillment of at least one deliverycriteria. In some embodiments, the identified service can be deliveredbased on data contained in the data stream received from the messagingbus 56, which information contained in the data stream of the messagingbus 56 can serve as a trigger for delivery of the service and/or of theservice component. As discussed above with respect to block 910. In someembodiments, the identified service can be delivered through themultimodal system 100, and/or via a source associated with and/or usingthe multimodal system 100.

After the identified service has been delivered, the process 900proceeds to block 914, wherein one or several updates are received. Insome embodiments, these updates can be update inputs which can bereceived and a similar manner to the inputs received in block 902discussed above. In some embodiments, these updates can correspond toupdates for at least some of the n-dimension attributes of thefirst-user, and in some embodiments, these updates can be received fromthe source device 136.

After the updates have been received, the process 900 proceeds to block916, wherein regeneration of the characterization is triggered. In someembodiments, the regeneration of the characterization vector can betriggered when a predetermined amount of updates have been received,when a predetermined time period has passed, and/or based on thereceived input requesting regeneration of the characterization vector.After the regeneration of the characterization vector has beentriggered, the process 900 proceeds to block 918, wherein thecharacterization vector is regenerated. In some embodiments, thecharacterization vector can be regenerated from the n-dimensionattributes of the first user. In some embodiments, these n-dimensionattributes can include the update to some of the n-dimension attributesof the first user. In some embodiments, the regeneration of thecharacterization vector can be performed in a similar manner to thegeneration of the characterization vector discussed in block 906.

After the characterization vector has been regenerated, the process 900proceeds to block 920 wherein user progress is determined. In someembodiments, user progress can be determined by a comparison of thecharacterization vector generated in block 906 to the regeneratedcharacterization vector. In some embodiments, and as a part of thiscomparison, a discrepancy vector can be generated, which discrepancyvector can characterize the difference between the characterizationvector generated in block 906. In the regenerated characterizationvector.

In some embodiments, after the determination of user progress, theprocess 900 can proceed to block 922 wherein an efficacy of the serviceis determined. In some embodiments, this efficacy can be determinedbased on the comparison of block 920, and specifically based on thediscrepancy vector. In some embodiments, the determined efficacy can beused to update all or portions of the action database 117, including,for example, the conditional probabilities blinking actions and/orservices, to all or portions of the characterization vector of thefirst-user.

As indicated at block 924, in some embodiments, services can beidentified for provisioning to additional subject-users during or afterthe completion of some or all of steps 902 through 922. In someembodiments, the identifying and/or provisioning of services, to one orseveral additional subject-users can include, for example, the receiptof inputs relating to a second subject-user, the generating and/orstoring of a profile for the second subject-user, the generating of acharacterization vector for the second subject-user, the identifying ofone or several services for provisioning to the second subject-user,and/or the delivery of those identified services to the secondsubject-user. In some embodiments, services for the second subject-usercan be identified based on the determined efficacy of the servicesprovision to the first subject-user, and specifically according toupdates to the action database 117, subsequent to the determination ofefficacy of services indicated in block 922.

With reference now to FIG. 15, a flowchart illustrating one embodimentof a process 930 for multimodal remediation is shown. The process 930can be performed by all or portions of the automated provisioningnetwork 10 including, for example, all or portions of the multimodalprovisioning system 100. The process 930 begins a block 932 wherein oneor several inputs are received. In some embodiments, each of these oneor several inputs can be received from one or several devices 136, 140,141, 148. In some embodiments, the inputs can be received by the centralserver 112, and in some embodiments, some or all of these inputs canrelate to a single subject-user.

After the input has been received, the process 900 proceeds to block904, wherein the user profile is generated, updated, and/or stored basedon the received inputs. In some embodiments, the user profile can begenerated and/or updated by the central server 112 and can be stored inthe user profile database 116. The user profile can relate to the singlesubject-user for whom some or all of the inputs in block 902 werereceived. In some embodiments, the user profile identifies n-dimensionattributes of the single subject-user for whom some or all of the inputsin block 902 were received. In some embodiments, the singlesubject-user, is referred to as a first subject-user.

At block 936, a current state of the subject-user is determined. In someembodiments, the current state of the user can be determined accordingto all or portions of the user profile and/or according to thecharacterization vector can be generated according to block 906 of FIG.14. The current state of the subject-user can be determined by thecentral server 112.

After the current state of the subject-user is determined, the process930 proceeds to block 938, wherein a risk profile is generated for thesubject-user. In some embodiments, the risk profile can be generatedaccording to the current state of the subject-user by, for example, thecentral server 112. The risk profile can identify and/or characterize alikelihood of an adverse outcome to the subject-user. The risk profilecan be generated according to a machine learning model that receivesuser attributes as inputs and outputs a risk profile which can include alevel of risk, and events associated with that risk level. In someembodiments, the risk profile can identify and/or characterize alikelihood of an adverse outcome to the subject-user within a common,for example, predetermined timeframe.

After the risk profile has been generated, the process 930 proceedsblock 940, wherein a remediation is identified. In some embodiments, theremediation can correspond to the service identified in block 908 ofFIG. 14. In some embodiments, this remediation can be identified via,for example, an AI machine-learning model, and this remediation has beenidentified and/or selected to mitigate the likelihood of the adverseoutcome. In some embodiments, the remediation can be identified based onuser profile and/or the current state of the subject-user. Theremediation can be identified by the central server 112.

After the remediation has been identified, the process 930 proceeds todecision state 942 wherein it is determined if there is a datainsufficiency for the subject-user. In some embodiments, this datainsufficiency can comprise one or several missing attributes of thesubject-user. In some embodiments, this data insufficiency can prevent,for example, at least one of the completing of the determination of thecurrent state of the subject-user, the completing of the generation ofthe risk profile, and/or the completing of identification of theremediation. In some embodiments, for example, this data insufficiencymay not prevent the determination of the current state of thesubject-user, the generation of the risk profile, and/or theidentification of the remediation, but this data insufficiency mayprevent the performing of these steps at a desired confidence level,and/or accuracy level. In some embodiments, the data insufficiency canbe evaluated by the server 112.

If it is determined that there is a data insufficiency, then the process930 proceeds to block 944, wherein the data insufficiency is identified.After the data insufficiency has been identified, the process 930proceeds to block 946 wherein a remedial service is identified. In someembodiments, the remedial service can be a service for provisioning tothe first user and/or to the subject-user, which service is identifiedto remedy the data insufficiency. In some embodiments, this service cancomprise a digital component, and/or a non-digital component. In someembodiments, the digital component can comprise a plurality of timedand/or automatically triggered reminders. In some embodiments, some orall of these reminders can direct the subject-user to complete anaction, which action can be associated with the non-digital component ofthe service. In some embodiments, the remedial service can be identifiedby the server 112 identifying attributes of the missing data giving riseto the data insufficiency and identifying services through which datacorresponding to those attributes is generated and/or determined.

After the remedial service has been identified, the process 930 proceedsto block 948, wherein the remedial service is delivered. In someembodiments, this can include delivery of the digital component of theservice, and/or of the non-digital component. In some embodiments, thedigital component can be automatically delivered, and specifically canbe automatically delivered subsequent to the automated determination offulfillment of at least one delivery criteria as discussed in greaterdetail with respect to block 910 and 912 of FIG. 14.

After the remedial service has been delivered, the process 930 proceedsto block 950, wherein the data insufficiency is resolved. In someembodiments, this can include the updating of the user profile withinputs generated from the delivery of the remedial service and/or withinputs generated subsequent to delivery of the remedial service. In someembodiments, and as part of the resolving of the data insufficiency, thepreviously uncompleted one or more of: determination of the currentstate of the subject-user, generation of the risk profile of thesubject-user, and/or identification of the remediation, is completed.

After the data insufficiency has been resolved, the process 930 returnsto decision state 942 wherein it is determined if the data insufficiencystill exists. If it is determined that there is no data insufficiency,then the process 930 proceeds to block 952 wherein the remediation isdelivered. After the remediation has been delivered, the efficacy of theremediation can be determined and block 954. In some embodiments, theefficacy can be determined similar to the determination block 922 ofFIG. 14.

As indicated at block 956, in some embodiments, services can beidentified for provisioning to additional subject-users during or afterthe completion of some or all of steps 932 through 954. In someembodiments, the identifying and/or provisioning of services, to one orseveral additional subject-users can include, for example, the receiptof inputs relating to a second subject-user, the generating and/orstoring of a profile for the second subject-user, the generating of acharacterization vector for the second subject-user, the identifying ofone or several services for provisioning to the second subject-user,and/or the delivery of those identified services to the secondsubject-user. In some embodiments, services for the second subject-usercan be identified based on the determined efficacy of the servicesprovisioned to the first subject-user, and specifically according toupdates to the action database 117 subsequent to the determination ofefficacy of services indicated in block 954.

With reference now to FIG. 16, a flowchart illustrating one embodimentof a process 960 for scheduling, and specifically for automaticscheduling of service provisioning is shown. In some embodiments, theprocess 960 can be performed as a part of, or in the place of one orboth of steps 948, 952. The process 960 begins at block 962, whereinuser location information is retrieved. In some embodiments, this caninclude current location information and/or location historyinformation. In some embodiments, the location history information canidentify user location information over a past period of time, and insome embodiments, the location history information can identify historictrends in the user location and/or the user movement. This can includeidentifying locations the user regularly frequents at certain times,travel routes, normal times of travel, or the like. The locationinformation can be retrieved from the user device 140 and/or from theuser profile database 116.

After the location information is retrieved, the process 960 proceeds toblock 964, wherein service provisioning classification is determined. Insome embodiments, this can include determining classification of theservice for provisioning, and specifically determining one or severalattributes of the service for provisioning. In some embodiments, thisclassification can identify one or several attributes of the servicelocation from which the service can be received. These attributes canspecify, for example, the type of location such as, for example, ahospital, surgical center, a clinic, or the like. In some embodiments,this information can be retrieved from the action database 117.

After the service provisioning classification has been determined, theprocess 960 proceeds to block 966 wherein potential service locationsare determined. In some embodiments, these potential service locationsare determined based on a combination of the location of potentialservice locations, user location information, service classification,and/or service location attributes. In some embodiments, the servicelocation attributes can identify one or several service types providedat a service location. In some embodiments, the identification ofpotential service locations can include identifying one or severalservice locations that are conveniently located with respect to the userlocation history and that provide services corresponding to the remedialservice and/or to the remediation.

After potential service locations of an identified, the process 960proceeds to block 968 wherein availability information for the user, aswas for the potential service locations is identified and unmatched. Insome embodiments, this can include matching the schedule of thesubject-user to the schedule of sources at the service location. In someembodiments, this can further include matching the schedule of thesubject-user when the subject-user is close to one of the potentialservice locations to the schedule of sources at that one of thepotential service locations. This matching can be performed by thecentral server 112.

With reference now to FIG. 17, a flowchart illustrating one embodimentof a process 1000 for automatic customized output generation delivery isshown. The process 1000 can be performed by all or portions of theautomated provisioning network 10 including, for example, all orportions of the multimodal provisioning system 100. The process 1000begins at block 1002, wherein an attribute of the subject-user isreceived. In some embodiments, the attribute of the subject-user can bereceived from one of the devices 136, 140, 141, 148. In someembodiments, this attribute can be generated and/or provided based on aprovided service, based on an input from the subject-user to the userdevice 140, and/or from a source to the provider device 136. In someembodiments, this attribute can be received from testing equipment suchas from medical testing equipment. In some embodiments, this medicaltesting equipment, also referred to herein as an electrical analysismachine, generates data indicative of the attribute of the subject-userfrom an interaction with the subject-user. In some embodiments, theelectrical analysis machine automatically provides the data indicativeof the attribute of the subject-user to the server 112.

In some embodiments, at least some of the attributes of the subject-usercan be received form the source device 136. In some embodiments, forexample, the attribute can characterize a clinical visit, treatment,medication, procedure, diagnosis, or the like, provided to or for thesubject-user. After the attribute of the subject-user has been received,the attribute can be stored in the user profile database 116 and can be,in some embodiments, added to the user profile.

At block 1004, a report request is received by the server 112. Thisreport request can include information identifying an attribute of therecipient-user. In some embodiments, the report request can be receivedby the server 112 from one of the devices 136, 140, 141, 148. In someembodiments, the report request can be received from a recipient device141, which recipient device can be any device used by any class of userto request a report from the multimodal provisioning system 100 and/orfrom the automated provisioning network 10. In some embodiments, usersof the multimodal provisioning system 100 and/or from the automatedprovisioning network 10 can each belong to one or more of one or severalclasses. In some embodiments, these classes are fixed classes that areassociated with the role of each user in providing services to thesubject-user. Thus, in some embodiments, one class can be subject-users,one class can be sources, one class can be insurers, one class can beregulators, and/or one class can be administrators. In some embodiments,the classes can be customized based on attributes of users. In someembodiments, these classes can include at least one class ofsubject-users and at least two-classes of recipient-users. Theseattributes can include, for example, level of sophistication,information relating to degree of follow-up and/or requests for furtherinformation have been made by the user, or the like.

After the report request has been received, the process 1000 proceeds toblock 1006, wherein attributes of the recipient-user are received. Insome embodiments, these attributes of the recipient-user can be embeddedin the report request and specifically can be embedded in metadata ofthe report request. In some embodiments, this attribute of therecipient-user can identify the one or several classes to which therecipient-user belongs, and/or the attribute can be used to identify theone or several classes to which the recipient-user belongs.

After the attribute of the recipient-user has been received, the process1000 proceeds to block 1008, wherein a machine-learning model isselected. In some embodiments, the multimodal provisioning system 100can include a model database 119, which model database 119 can beaccessed by the server 112. The model database 119 can comprise aplurality of machine-learning models, which can be natural languagegeneration models, that are trained to generate text. In someembodiments, these machine-learning models can be trained to generatetext in response to inputs characterizing attributes of at least onesubject-user and at least one recipient-user. In some embodiments, eachof the classes of users can be associated with at least one of themachine-learning model such that when a class of recipient-user isidentified, a corresponding machine-learning model can be identified. Insome embodiments, the machine-learning model can be selected bycomparing the class of the recipient-user to metadata associated witheach of the machine-learning models. In some embodiments, themachine-learning model can be selected by querying the model database119 for a model corresponding to the class of the recipient-user.

In some embodiments, the selecting of the machine-learning model caninclude the ingestion of the information into the machine-learning modelas indicated in block 1010. In some embodiments, this information cancomprise received attributes of the subject-user and/or attributes ofthe recipient-user. In some embodiments, for example, informationresulting from a service provided to the subject-user can be ingestedinto the machine-learning model. In some embodiments, this ingestion caninclude the generation of a vector and/or tensor comprising theinformation relating to the subject-user and the inputting of the vectorand/or tensor into the machine-learning model.

After ingesting the received attributes, the process 1000 proceeds toblock 1012, wherein a text output is received from the machine-learningmodel. In some embodiments, the text output can from themachine-learning model can be the text generated by the machine learningmodel. In some embodiments, the text output can be customized to therecipient-user. After the output text is received, the process 1000proceeds to block 1014, wherein a message containing the text output isgenerated and deliver. In some embodiments, the message can be generatedand delivered by the server 112. The message can comprise at leastportions of the text output of the machine-learning model. The messagecan be generated and/or sent by the communications subsystem 86 of theserver 112.

After the message containing the output text has been generated and/orsent, the process 1000 proceeds to block 1016, wherein report feedbackis received. In some embodiments, the report feedback can includeinformation indicate whether and/or the degree to which the generatedand/or delivered message responded to the report request of block 1004.In some embodiments, the report feedback can be received by the server112 from the recipient device 141 from which the report requestoriginated.

After the report request has been received, the process 1000 proceeds toblock 1018, wherein the training of the machine-learning model used togenerate the output text is updated. In some embodiments, this caninclude modifying aspects of the machine-learning model such as, forexample, one or several weightings within the machine-learning model. Insome embodiments, this modifying of the training of the machine-learningmodel can be automatically performed and/or the training of themachine-learning model can be updated based on the received reportfeedback.

With reference now to FIG. 18, a swim lane diagram of one embodiment ofa process 1030 for automatic customized output generation delivery isshown. The process 1030 can be performed by all or portions of theautomated provisioning network 10 including, for example, all orportions of the multimodal provisioning system 100. The process 1030begins at block 1032, wherein data is received by a device, which can bethe source device 136. In some embodiments, this data can characterizean attribute of the subject-user. This data can be converted into one orseveral communications and/or signals and send to the server 102 asindicated in block 1034.

At block 1036, the one or several communications sent in block 1034 arereceived by the server 112 in block 1036. In some embodiments, and asindicated in blocks 1052 and 1054, a report request can be received by arecipient device 141 and can be sent to the server 112 as indicated inblock 1054. In some embodiments, a first report request can be receivedby a first recipient device 141 at a first time and a second reportrequest can be received by a second recipient device 141 at a secondtime. At block 1038, one or several of these report requests can bereceived by the server 112 from one or more of the recipient devices141. In one embodiment, a first report request can be received from afirst recipient device 141 at a first time.

After the report request is received, the process 1030 proceeds to block1040, wherein a recipient attribute is received. In some embodiments,this recipient attribute can identify a class of the recipient-user. Insome embodiments, this recipient attribute can be received simultaneouswith receipt of the report request. In one embodiment, for example, therecipient attribute can accompany the report request.

After the recipient attribute has been received, the process 1030proceeds to block 1042, wherein a machine-learning model is selectedbased on the received recipient attribute. This machine-learning modelcan be selected from machine-learning models stored in the modeldatabase 119. After the machine-learning model has been selected, theprocess 1030 proceeds to block 1044, wherein inputs are ingested intothe machine-learning model, which ingestion leads to themachine-learning model outputting text. These outputs are received inblock 1046 by the server 112. In some embodiments, the outputting oftext by the machine-learning model can be indicated in the data streamof the messaging bus 56. In some embodiments, these one or severalindicia of the outputting of the text can trigger the generation anddelivery of a message to the recipient-user device 141 from which thereport request was received.

In some embodiments, the generated and/or delivered message can bereceived by the recipient-user device 141 from which the report requestwas received. In response to the receipt of this communication, therecipient-user device receiving the message can receive feedback datafrom the recipient-user. In some embodiments, this feedback data cancharacterize the degree to which the communication is responsive to thereport request. The feedback data can be provided to the server 112 fromthe recipient-user device 141, and this data can be used to update themachine-learning model.

In some embodiments, a second report request can be received by theserver 112 from a second recipient-user device 141. The second reportrequest can comprise at least one input identifying an attribute of thesecond recipient-user. In some embodiments, the attribute of the secondrecipient-user is different than the attribute of the firstrecipient-user. In some embodiments, this different attribute can resultin the second recipient-user having a different class than the firstrecipient-user.

In some embodiments, and after receiving the second report request, theserver 112 can select a second machine-learning model. This secondmachine-learning model can be different than the first machine-learningmodel due to the different attribute and/or different class of thesecond recipient-user. The server 112 can then ingest inputs into thesecond machine-learning model, can received outputted text from thesecond machine-learning model, and can automatically generate anddeliver a second message to the second recipient-user device 141 and thesecond recipient-user, which message can comprise all or portions of theoutput of the second machine-learning model. In some embodiments, theinputs ingested into the second machine-learning model relating to thesubject-user can be the same inputs ingested into the firstmachine-learning model. However, in some embodiments, due to theselection of the second machine learning model, which secondmachine-learning model is different than the first machine-learningmodel, the outputs of the second machine-learning model can be differentthan the outputs of the first machine-learning model. In someembodiments, this text output of the second machine-learning model canbe customized to the second recipient-user.

Specific details are given in the above description to provide athorough understanding of the embodiments. However, it is understoodthat the embodiments may be practiced without these specific details.For example, circuits may be shown in block diagrams in order not toobscure the embodiments in unnecessary detail. In other instances,well-known circuits, processes, algorithms, structures, and techniquesmay be shown without unnecessary detail in order to avoid obscuring theembodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine-readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more memories for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine-readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, and/or various otherstorage mediums capable of storing that contain or carry instruction(s)and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A machine-learning input-based dataautogeneration system comprising: a memory comprising: a user profiledatabase identifying attributes of a plurality of users, wherein theplurality of users comprises a plurality of classes of users, whereinthe plurality of classes of users comprise at least one class ofsubject-users and at least two classes of recipient-users; a modeldatabase comprising a plurality of machine-learning models, each of theplurality of machine-learning models trained to generate a report inresponse to received inputs characterizing attributes of a subject-userand at least one recipient-user; a source device configured to receivesource inputs and transmit the received source inputs, wherein thesource inputs relate to an attribute of the subject-user; a recipientdevice configured to receive recipient inputs and transmit the receivedrecipient inputs, wherein the recipient inputs identify an at least oneattribute of a recipient-user; and a server configured to: generate acharacterization vector characterizing a patient state; identify a statenode in a first network corresponding to the patient state, the firstnetwork comprising a plurality of nodes linked via a plurality of edges,wherein each of the edges links a pair of nodes, wherein each of theplurality of edges is associated with a conditional probability of atruth of one of the nodes in a pair of nodes linked by the edge based ona status of the other of the nodes in the pair of nodes; identify anaction node in a second network according to machine-learning model, theaction node linked to the state node; automatically update a userschedule database with information characterizing scheduling ofprovisioning of a medical service associated with the identified actionnode; deliver the medical service associated with the identified actionnode; receive a request at the server for a report characterizingattributes of the subject-user and the delivered medical service;receive a plurality of inputs identifying attributes of thesubject-user; receive at least one input identifying an attribute of therecipient-user; select a machine-learning model based on a comparison ofthe at least one attribute of the recipient-user to metadata linked withthe machine-learning model; ingest received inputs into themachine-learning model; receive the generated report from themachine-learning model, wherein the generated report is customized tothe at least one attribute of the recipient-user; and automaticallygenerate and deliver a message to the recipient device comprising atleast portions of the generated report.
 2. The system of claim 1,wherein the request for the report comprises data identifying the atleast one attribute of the recipient-user.
 3. The system of claim 2,wherein at least some of the plurality of inputs identifying attributesof the subject-user are received from the source device.
 4. The systemof claim 3, further comprising an electrical analysis machine, whereinthe electrical analysis machine automatically generates data indicativeof an attribute of the subject-user from an interaction with thesubject-user.
 5. The system of claim 4, wherein the electrical analysismachine automatically provides the data indicative of the attribute ofthe subject-user to the server.
 6. The system of claim 5, wherein theserver is further configured to receive feedback data indicative of aresponsiveness of the delivered message to the request for the report.7. The system of claim 6, wherein the server is further configured toreceive a second request for a report from a second recipient-user. 8.The system of claim 7, wherein the second request for the reportcomprises at least one input identifying an attribute of the secondrecipient-user, wherein the attribute of the second recipient-user isdifferent than the attribute of the recipient-user.
 9. The system ofclaim 8, wherein the server is further configured to: select a secondmachine-learning model based on the attribute of the secondrecipient-user; and automatically generate and deliver a second messageto the second recipient-user comprising an output of the secondmachine-learning model.
 10. The system of claim 9, wherein the server isfurther configured to: ingest the plurality of inputs identifyingattributes of the subject-user into the second machine-learning model;and receive text output from the second machine-learning model, whereinthe text output is customized to the second recipient-user.
 11. A methodof machine-learning input-based patient-report autogeneration, themethod comprising: generating a characterization vector characterizing apatient state; identifying a state node in a first network correspondingto the patient state, the first network comprising a plurality of nodeslinked via a plurality of edges, wherein each of the edges links a pairof nodes, wherein each of the plurality of edges is associated with aconditional probability of a truth of one of the nodes in a pair ofnodes linked by the edge based on a status of the other of the nodes inthe pair of nodes; identifying an action node in a second networkaccording to machine-learning model, the action node linked to the statenode; automatically updating a user schedule database with informationcharacterizing scheduling of provisioning of a medical serviceassociated with the identified action node; delivering the medicalservice associated with the identified action node; receiving a requestfor a report characterizing attributes of a subject-user and thedelivered medical service; receiving at a server a plurality of inputsfrom a service provisioning module on a source device, the plurality ofinputs identifying attributes of the medical service provided to asubject-user, wherein at least one of the plurality of inputs comprisesa note, wherein the plurality of inputs are received embedded in therequest for the report; receiving at the server at least one input froma recipient-user device identifying at least one attribute of arecipient-user; selecting a machine-learning model from a model databasebased on a comparison of the at least one attribute of therecipient-user to metadata linked with the machine-learning model, themodel database comprising a plurality of machine-learning models, eachof the plurality of machine-learning models trained to generate a reportin response to received inputs characterizing attributes of asubject-user and at least one recipient-user; ingesting received inputsinto the machine-learning model; receiving the generated report from themachine-learning model, wherein the generated report is customized tothe at least one attribute of the recipient-user; and automaticallygenerating and delivering a message to the recipient-user devicecomprising at least portions of the generated report.
 12. The method ofclaim 11, wherein the request for the report comprises data identifyingthe at least one attribute of the recipient-user.
 13. The method ofclaim 12, wherein at least some of the plurality of inputs identifyingattributes of the subject-user are received from the source device. 14.The method of claim 13, further comprising receiving data identifying anattribute of the subject-user at the server from an electrical analysismachine, wherein the electrical analysis machine automatically generatesdata from an interaction with the subject-user.
 15. The method of claim14, wherein the electrical analysis machine automatically provides thedata identifying the attribute of the subject-user to the server. 16.The method of claim 15, further comprising receiving feedback dataindicative of a responsiveness of the delivered message to the requestfor the report.
 17. The method of claim 16, further comprising receivinga second request for a report from a second recipient-user.
 18. Themethod of claim 17, wherein the second request for the report comprisesat least one input identifying an attribute of the secondrecipient-user, wherein the attribute of the second recipient-user isdifferent than the attribute of the recipient-user.
 19. The method ofclaim 18, further comprising: selecting a second machine-learning modelbased on the attribute of the second recipient-user; and automaticallygenerating and delivering a second message to the second recipient-usercomprising an output of the second machine-learning model.
 20. Themethod of claim 19, further comprising: ingesting the plurality ofinputs identifying attributes of the subject-user into the secondmachine-learning model; and receiving text output from the secondmachine-learning model, wherein the text output is customized to thesecond recipient-user.